CoSupport AI
What is Omyn?
Omyn addresses the challenges associated with traditional, manual Anti-Money Laundering (AML) compliance processes, which often result in inefficiency, overlooked high-risk cases, and costly errors. It provides an AI-powered solution engineered to automate these critical workflows for financial institutions and other regulated entities.
The platform leverages an API for efficient triage, escalation, and remediation of potential AML issues, complete with a comprehensive audit trail. This AI-driven approach aims to deliver faster, more accurate AML reporting while substantially reducing manual effort, potentially by up to 90%. By automating routine tasks, Omyn enables compliance teams to shift their focus towards strategic risk mitigation and oversight, integrating with existing tools through a human-centric workflow.
Features
- Automated Triage & Escalation: Uses AI to assess risk levels, identify suspicious patterns, recommend actions, and escalate cases.
- Intelligent Narrative Generation: Automatically creates compliant narratives for flagged cases and transactions.
- Case Management: Provides tools to manage AML cases including details, entities, and transactions.
- Full Transparency & Audit Trails: Records all actions for complete visibility and compliance auditing.
- Data Ingestion: Facilitates easy integration of relevant data into the compliance workflow.
- Human-Centric AI Workflow: Ensures human oversight remains central to the automated process.
- Integration Capability: Designed to integrate smoothly with existing tools and data flows.
Use Cases
- Automating AML case triage and prioritization.
- Generating compliant narratives for suspicious activity reports (SARs).
- Reducing manual workload for compliance teams.
- Improving the accuracy and speed of AML reporting.
- Enhancing auditability with detailed tracking.
- Streamlining escalation processes for high-risk cases.
- Improving overall AML compliance efficiency.
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Omyn Uptime Monitor
Average Uptime
99.93%
Average Response Time
233.47 ms
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